from pyod.utils.data import generate_data from pyod.utils.data import evaluate_print我们生成具有预定义离群率的合成数据来模拟异常值。 contamination = 0.1 # percentage of outliers n_train = 200 # number of training points n_test
frompyod.models.knnimportKNNfrompyod.utils.dataimportgenerate_data# 设置异常值比例和训练、测试样本数量contamination=0.1# 异常值的百分比n_train=200# 训练样本数量n_test=100# 测试样本数量# 生成训练和测试数据集,包含正常数据和异常值,默认输入数据特征维度为2,标签为二进制标签(0: 正常点, 1: 异常点)# ...
from pyod.utils.data import generate_data, get_outliers_inliers #generate random data with two features X_train, Y_train = generate_data(n_train=200,train_only=True, n_features=2) # by default the outlier fraction is 0.1 in generate data function outlier_fraction = 0.1 # store outliers a...
from pyod.utils.utility import standardizer from pyod.utils.data import generate_data from pyod.utils.data import evaluate_print X, y = generate_data(train_only=True) # load data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.4) # standardizing data for proces...
from pyod.models.knn import KNN from pyod.utils.data import generate_data from pyod.utils.data import evaluate_print 我们生成具有预定义离群率的合成数据来模拟异常值。 contamination = 0.1 # percentage of outliers n_train = 200 # number of training points n_test = 100 # number of testing poin...
from pyod.utils.data import generate_data from pyod.utils.data import evaluate_print 1. 2. 3. 我们生成具有预定义离群率的合成数据来模拟异常值。 contamination = 0.1 # percentage of outliers n_train = 200 # number of training points
from pyod.utils.data import generate_data contamination = 0.1 # percentage of outliers n_train = 200 # number of training points n_test = 100 # number of testing points X_train, X_test, y_train, y_test = generate_data(n_train=n_train, ...
from pyod.models.knnimportKNNfrom pyod.utils.dataimportgenerate_data from pyod.utils.dataimportevaluate_print 我们生成具有预定义离群率的合成数据来模拟异常值。 代码语言:javascript 代码运行次数:0 运行 AI代码解释 contamination=0.1# percentageofoutliers ...
from pyod.utils.data import generate_data, get_outliers_inliers #generate random data with two features X_train, Y_train = generate_data(n_train=200,train_only=True, n_features=2) # by default the outlier fraction is 0.1 in generate data function ...
from pyod.utils.data import generate_data X_train, X_test, y_train, y_test = generate_data(n_train=200, n_test=100, contamination=0.1)```在这里，我们导入了KNN模型，并使用generate\_data函数生成了训练和测试数据。这些数据将用于后续的异常检测任务。❒ **2.2 KNN模型示例** 通过KNN模型...